Taming computational complexity: efficient and parallel simrank optimizations on undirected graphs
WAIM'10 Proceedings of the 11th international conference on Web-age information management
ASAP: towards accurate, stable and accelerative penetrating-rank estimation on large graphs
WAIM'11 Proceedings of the 12th international conference on Web-age information management
A space and time efficient algorithm for SimRank computation
World Wide Web
Mining tribe-leaders based on the frequent pattern of propagation
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
Delta-SimRank computing on MapReduce
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Does social contact matter?: modelling the hidden web of trust underlying twitter
Proceedings of the 22nd international conference on World Wide Web companion
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SimRank has been proposed to rank web documents based on a graph model on hyperlinks. The existing techniques for conducting SimRank computation adopt an iteration computation paradigm. The most efficient technique has the time complexity $O\left(n^3\right)$ with the space requirement $O\left(n^2\right)$ in the worst case for each iteration where $n$ is the number of nodes (web documents). In this paper, we propose novel optimization techniques such that each iteration takes the time $O \left(\min \left\{ n \cdot m , n^r \right\}\right)$ and requires space $O \left( n + m \right)$ where $m$ is the number of edges in a web-graph model and $r \leq \log_2 7$. We also show that our algorithm accelerates the convergence rate of the existing techniques. Moreover, our algorithm not only reduces the time and space complexity of the existing techniques but is also I/O efficient. We conduct extensive experiments on both synthetic and real data sets to demonstrate the efficiency and effectiveness of our iteration techniques.